Recent Advances in Federated Learning for Connected Autonomous Vehicles: Addressing Privacy, Performance, and Scalability Challenges

Abstract

This review paper systematically explores the use of Federated Learning (FL) in Connected Autonomous Vehicles (CAVs) to improve vehicle performance, safety and user experience. FL presents a decentralized infrastructure that allows collaborative learning, while also ensuring data privacy, as CAVs increasingly rely on machine learning to process large amounts of sensor data. FL has been demonstrated to contribute to cooperative perception, vehicle trajectory prediction, traffic flow optimization, predictive maintenance, personalized in vehicle experiences, and advanced driver assistance systems (ADAS) through recent advances. Secondly, FL speeds up anomaly and intrusion detection, critical safety and security matters in CAV ecosystems. However, despite all these advancements, there are still challenges of data heterogeneity, communication overhead, security vulnerabilities, model convergence as well as regulatory compliance that constitute major barriers to FL deployment. This paper synthesizes recent contributions on this topic, identifies some gaps in the current research, and proposes future directions to fill those gaps. Federated Learning is a promising technology path towards driving innovations and improving the power and effectiveness of Autonomous Transportation Systems.